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library(GeoPressureR)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(kableExtra)
library(plotly)
library(GeoLocTools)
setupGeolocation()
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/2_light/", params$gdl_id, "_light_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_wind_graph.Rdata"))
col <- rep(RColorBrewer::brewer.pal(8, "Dark2"), times = ceiling(max(pam$sta$sta_id) / 8))Settings used
All the results produced here are generated with (1) the raw geolocator data, (2) the labeled files of pressure and light and (3) the parameters listed below.
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kable(gpr) %>% scroll_box(width = "100%")| gdl_id | keep | multisensor | crop_start | crop_end | thr_dur | extent_N | extent_W | extent_S | extent_E | map_scale | map_max_sample | map_margin | prob_map_s | prob_map_s_calib | prob_map_thr | shift_k | kernel_adjust | calib_lon | calib_lat | calib_1_start | calib_1_end | calib_2_start | calib_2_end | calib_2_lon | calib_2_lat | prob_light_w | thr_prob_percentile | thr_gs | thr_as | low_speed_fix | ringNo | scientific_name | common_name | mass | wing_span |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28AD | TRUE | TRUE | 2021-08-18 | 2022-06-30 | 0 | 0 | 34 | -16 | 42 | 10 | 300 | 40 | 0.7 | NA | 0.9 | 0 | 1.4 | 39.9889 | -3.37827 | 2021-08-18 | 2021-12-06 | 2022-04-30 | 2022-06-30 | NA | NA | 0.09 | 0.9 | 120 | 100 | 15 | BB6541 | Halcyon senegaloides | Mangrove Kingfisher | NA | NA |
Pressure timeserie
The labeling of pressure data is illustrated with this figure. The black dots indicates the pressure datapoint not considered in the matching. Each stationary period is illustrated by a different colored line.
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pressure_na <- pam$pressure %>%
mutate(obs = ifelse(isoutlier | sta_id == 0, NA, obs))
p <- ggplot() +
geom_line(data = pam$pressure, aes(x = date, y = obs), colour = "grey") +
geom_point(data = subset(pam$pressure, isoutlier), aes(x = date, y = obs), colour = "black") +
# geom_line(data = pressure_na, aes(x = date, y = obs, color = factor(sta_id)), size = 0.5) +
geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0), aes(x = date, y = pressure0, col = factor(sta_id))) +
theme_bw() +
scale_colour_manual(values = col) +
scale_y_continuous(name = "Pressure(hPa)")
ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)Pressure calibration
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pressure_ts_bind <- do.call("rbind", shortest_path_timeserie) %>%
filter(!is.na(sta_id))
pam$pressure %>%
left_join(pressure_ts_bind %>% dplyr::select(c("date","pressure0")), by="date") %>%
mutate(diff=ifelse(is.na(pressure0), 0, obs-pressure0)) %>%
filter(sta_id > 0 & !isoutlier) %>%
group_by(sta_id) %>%
mutate(sta_id = paste0(sta_id, " (SD=",round(sd(diff),2)," ; N=",n(),")")) %>%
ggplot( aes(x=diff)) +
geom_histogram(aes(y=(..count..)/tapply(..count..,..PANEL..,sum)[..PANEL..]), binwidth=.2) +
facet_wrap(~sta_id) +
scale_x_continuous(name = "Pressure Geolocator - best match ERA5 (hPa)") +
scale_y_continuous(name = "Normalized histogram")
Light
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raw_geolight <- pam$light %>%
transmute(
Date = date,
Light = obs
)
lightImage(tagdata = raw_geolight, offset = 0)
tsimagePoints(twl$twilight,
offset = 0, pch = 16, cex = 1.2,
col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
abline(v = gpr$calib_2_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_2_end, lty = 2, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_end, lty = 2, col = "firebrick", lwd = 1.5)
The probability map resulting from light data alone can be seen below.
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li_s <- list()
l <- leaflet(width = "100%") %>%
addProviderTiles(providers$Stamen.TerrainBackground) %>%
addFullscreenControl()
for (i_r in seq_len(length(light_prob))) {
i_s <- metadata(light_prob[[i_r]])$sta_id
info <- pam$sta[pam$sta$sta_id == i_s, ]
info_str <- paste0(i_s, " | ", info$start, "->", info$end)
li_s <- append(li_s, info_str)
l <- l %>% addRasterImage(light_prob[[i_r]], opacity = 0.8, colors = "OrRd", group = info_str)
}
l %>%
addCircles(lng = gpr$calib_lon, lat = gpr$calib_lat, color = "black", opacity = 1) %>%
addLayersControl(
overlayGroups = li_s,
options = layersControlOptions(collapsed = FALSE)
) %>%
hideGroup(tail(li_s, length(li_s) - 1))Light vs Pressure
We can compare light and pressure location at long stationary stopover (>5 days). By assuming the best match of the pressure to be the truth, we can plot the histogram of the zenith angle and compare to the fit of kernel density at the calibration site.
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raw_geolight <- pam$light %>%
transmute(
Date = date,
Light = obs
)
dur <- unlist(lapply(pressure_prob, function(x) difftime(metadata(x)$temporal_extent[2], metadata(x)$temporal_extent[1], units = "days")))
long_id <- which(dur > 5)
par(mfrow = c(2, 3))
for (i_s in long_id) {
twl_fl <- twl %>%
filter(!deleted) %>%
filter(twilight > shortest_path_timeserie[[i_s]]$date[1] & twilight < tail(shortest_path_timeserie[[i_s]]$date, 1))
sun <- solar(twl_fl$twilight)
z_i <- refracted(zenith(sun, shortest_path_timeserie[[i_s]]$lon[1], shortest_path_timeserie[[i_s]]$lat[1]))
hist(z_i, freq = F, main = paste0("sta_id=", i_s, " | ", nrow(twl_fl), "twls"))
lines(fit_z, col = "red")
xlab("Zenith angle")
}
Similarly, we can plot the line of sunrise/sunset at the best match of pressure (yellow line) and compare to the raw and labeled light data.
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lightImage(
tagdata = raw_geolight,
offset = gpr$shift_k / 60 / 60
)
tsimagePoints(twl$twilight,
offset = gpr$shift_k / 60 / 60, pch = 16, cex = 1.2,
col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
for (ts in shortest_path_timeserie) {
twl_fl <- twl %>%
filter(twilight > ts$date[1] & twilight < tail(ts$date, 1))
if (nrow(twl_fl) > 0) {
tsimageDeploymentLines(twl_fl$twilight,
lon = ts$lon[1], ts$lat[1],
offset = gpr$shift_k / 60 / 60, lwd = 3, col = adjustcolor("orange", alpha.f = 0.5)
)
}
}
Stationay period information
| sta_id | start | end |
|---|---|---|
| 1 | 2021-08-18 00:00:00 | 2021-12-05 17:00:00 |
| 2 | 2021-12-05 22:50:00 | 2021-12-07 17:50:00 |
| 3 | 2021-12-08 02:00:00 | 2021-12-09 20:00:00 |
| 4 | 2021-12-10 01:40:00 | 2021-12-10 17:40:00 |
| 5 | 2021-12-11 02:10:00 | 2021-12-11 21:20:00 |
| 6 | 2021-12-11 22:20:00 | 2021-12-16 19:40:00 |
| 7 | 2021-12-17 01:40:00 | 2022-01-04 19:40:00 |
| 8 | 2022-01-04 23:20:00 | 2022-04-27 18:20:00 |
| 9 | 2022-04-28 02:00:00 | 2022-04-28 16:50:00 |
| 10 | 2022-04-29 01:40:00 | 2022-04-29 20:50:00 |
| 11 | 2022-04-29 23:00:00 | 2022-06-29 23:50:00 |